{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# transformer-自然语言处理\n",
    "\n",
    "Transformer是近年来自然语言处理中非常重要的注意力机制模型,其基本原理及应用场景可以概括如下:\n",
    "\n",
    "1. 原理\n",
    "\n",
    "Transformer由编码器(Encoder)和解码器(Decoder)组成。主要的改进是完全利用注意力机制来捕捉词语之间的依赖关系,取代了RNN/CNN结构。\n",
    "\n",
    "Transformer的注意力机制允许模型学习词语之间的远程依赖关系。同时,Transformer也使用残差连接和层规范化来提升训练速度。\n",
    "\n",
    "2. 应用场景\n",
    "\n",
    "Transformer最初是作为神经机器翻译模型而提出,后来被证明在多个自然语言处理任务上都有出色表现。\n",
    "\n",
    "主要的应用场景包括:\n",
    "\n",
    "- 机器翻译:Transformer可以更好地建模长句子的语法结构。\n",
    "\n",
    "- 文本摘要:通过Encoder-Decoder结构进行抽象式文本摘要。\n",
    "\n",
    "- 语音识别:将语音信号转换为文本标注信息。\n",
    "\n",
    "- 自然语言推断:通过语义匹配判定文本之间的逻辑关系。\n",
    "\n",
    "- 问答系统:可以注意到问题和文档之间的语义匹配关系。\n",
    "\n",
    "综上,Transformer通过其高效的注意力机制,在多种自然语言处理任务上都表现优异,已成为该领域的主流模型。\n",
    "\n",
    "https://blog.csdn.net/qq_52785473/article/details/124537101\n",
    "\n",
    "https://blog.csdn.net/Datawhale/article/details/120320116"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"28.png\" width=\"400\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(url= \"28.png\",width=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"29.png\" width=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"29.png\",width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 词嵌入\n",
    "\n",
    "## 独热编码(one-hot encoding)\n",
    "\n",
    "在机器学习任务中,类别特征通常需要转换为独热编码(one-hot encoding)进行模型输入,主要有以下两个原因:\n",
    "\n",
    "1. 避免假定类别顺序。直接将类别特征用数字编码(比如0,1,2)会假定类别之间有大小顺序关系,而实际上类别仅仅是不同的类型,之间并无大小顺序可言。\n",
    "\n",
    "2. 线性可分。多分类任务需要模型输出维度等于类别数,而独热编码可以将类别扩展为一个个0/1的特征维度,使不同类别成为线性可分的。\n",
    "\n",
    "具体来说,对于一个有N个类别的特征,使用独热编码将其转换为一个N维0/1向量,其中类别的索引位置为1,其他位置为0。\n",
    "\n",
    "比如对颜色特征[\"红\",\"绿\",\"蓝\"]使用独热编码,可以将其转换为:\n",
    "\n",
    "红 -> [1, 0, 0] \n",
    "\n",
    "绿 -> [0, 1, 0]\n",
    "\n",
    "蓝 -> [0, 0, 1]\n",
    "\n",
    "这样模型就能够区分不同颜色,而不会对颜色顺序进行假设。因此,独热编码常用于处理机器学习模型中的类别特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>make</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>green</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blue</td>\n",
       "      <td>BMW</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yellow</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    color       make  year\n",
       "0   green  Chevrolet  2017\n",
       "1    blue        BMW  2015\n",
       "2  yellow      Lexus  2018"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([\n",
    "    ['green', 'Chevrolet', 2017],\n",
    "    ['blue', 'BMW', 2015],\n",
    "    ['yellow', 'Lexus', 2018],\n",
    "])\n",
    "df.columns = ['color', 'make', 'year']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>color_blue</th>\n",
       "      <th>color_green</th>\n",
       "      <th>color_yellow</th>\n",
       "      <th>make_BMW</th>\n",
       "      <th>make_Chevrolet</th>\n",
       "      <th>make_Lexus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  color_blue  color_green  color_yellow  make_BMW  make_Chevrolet  \\\n",
       "0  2017           0            1             0         0               1   \n",
       "1  2015           1            0             0         1               0   \n",
       "2  2018           0            0             1         0               0   \n",
       "\n",
       "   make_Lexus  \n",
       "0           0  \n",
       "1           0  \n",
       "2           1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_processed = pd.get_dummies(df, columns=['color', 'make'] ,dtype=int)\n",
    "df_processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自然语言处理的主要任务\n",
    "\n",
    "自然语言处理的主要任务包括:\n",
    "\n",
    "1. 文本分类:将文本划分到预定义的类别中,如情感分析、新闻分类等。常用方法有SVM、CNN、RNN等。\n",
    "\n",
    "2. 名实体识别(NER):识别文本中的名实体,如人名、地名、组织名等。通常把它作为序列标注问题来解决。\n",
    "\n",
    "3. 语言模型:给出前面的词序列,预测下一个词的概率。语言模型可应用于机器翻译、语音识别等任务。\n",
    "\n",
    "4. 文本生成:根据输入信息自动生成文本,如机器翻译、对话系统、文本摘要等。\n",
    "\n",
    "5. 信息抽取:从非结构化文本中提取结构化信息,如关系抽取、事件抽取等。\n",
    "\n",
    "6. 文本相似性:计算两个文本语义的相似程度,应用在相似文档检索等任务上。\n",
    "\n",
    "7. 问答系统:接收用户问题并从知识库中搜索答案,如聊天机器人。\n",
    "\n",
    "8. 篇章分析:分析文本的语法结构、核心词等要素,以获取文本的深层含义。\n",
    "\n",
    "9. 情感分析:判断一段文本所表达的情绪态度,即正面还是否定面。\n",
    "\n",
    "以上是自然语言处理的典型任务,还有语音识别、机器翻译等也属于该领域。实际应用中往往会集成多个技术来解决问题。\n",
    "\n",
    "## 词嵌入(word embedding)\n",
    "\n",
    "词嵌入(word embedding)是自然语言处理中的一个重要技术,它的思想是将词映射到一个连续的低维向量空间中,使得语义相似的词在这个空间中距离较近。\n",
    "\n",
    "https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html\n",
    "\n",
    "https://blog.csdn.net/raelum/article/details/125462028\n",
    "\n",
    "\n",
    "## pytorch 中的词嵌入nn.Embedding\n",
    "\n",
    "nn.Embedding和nn.Linear都是PyTorch中常用的层,两者的主要区别是:\n",
    "\n",
    "1. nn.Embedding用于处理离散特征,nn.Linear用于处理连续特征。\n",
    "\n",
    "2. nn.Embedding将整数索引映射到固定维度的稠密向量,nn.Linear将输入数据映射到输出维度。\n",
    "\n",
    "3. nn.Embedding输出维度由嵌入矩阵定义,nn.Linear输出维度由Linear层参数定义。\n",
    "\n",
    "4. nn.Embedding输入一般是词索引,nn.Linear输入可以是任意形状张量。\n",
    "\n",
    "上面例子展示了nn.Embedding和nn.Linear的不同之处:前者处理离散特征获得到词向量,后者用于连续特征的线性映射。两者在自然语言处理任务中经常联合使用。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"25.png\" width=\"400\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Image(url= \"25.png\",width=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"24.webp\" width=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"24.webp\",width=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"30.jpg\" width=\"800\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"30.jpg\",width=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "import math\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nn.Embedding\n",
    "embed = nn.Embedding(10, 64) # 10个词,embedding维度64 \n",
    "input = torch.LongTensor([1,5,8]) # 输入词索引\n",
    "embed_vector = embed(input) # 将词索引映射为词向量\n",
    "\n",
    "# nn.Linear \n",
    "fc = nn.Linear(32, 10) # 输入维度32,输出维度10\n",
    "input = torch.randn(8, 32) # 8个32维输入 \n",
    "output = fc(input) # 全连接层映射到10维输出"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 简单实现（在torch中并没有用独热，而是通过位置与nn.Embedding特殊的机制实现了类似独热的效果）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 1.5719, -1.1032,  0.6363, -1.1446, -0.7449]],\n",
      "       grad_fn=<EmbeddingBackward0>)\n"
     ]
    }
   ],
   "source": [
    "word_to_ix = {\"hello\": 0, \"world\": 1}\n",
    "embeds = nn.Embedding(2, 5)  # 2 words in vocab, 5 dimensional embeddings\n",
    "lookup_tensor = torch.tensor([word_to_ix[\"hello\"]], dtype=torch.long)\n",
    "hello_embed = embeds(lookup_tensor)\n",
    "print(hello_embed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[ 1.5719, -1.1032,  0.6363, -1.1446, -0.7449],\n",
       "        [-1.3902,  0.4242, -0.8732,  1.0635, -0.1250]], requires_grad=True)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeds.weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* transformer中的实现\n",
    "\n",
    "https://blog.csdn.net/qq_52785473/article/details/124537101"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Embeddings(nn.Module):\n",
    "    def __init__(self, d_model, vocab):\n",
    "    # d_model:词嵌入维度\n",
    "    # vocab:字典大小\n",
    "        super(Embeddings, self).__init__()\n",
    "        self.lut = nn.Embedding(vocab, d_model)\n",
    "        self.d_model = d_model\n",
    "    def forward(self, x):\n",
    "        return self.lut(x) * math.sqrt(self.d_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 4, 512])\n"
     ]
    }
   ],
   "source": [
    "d_model = 512  # embedding_size\n",
    "vocab = 1000  # 词典大小\n",
    "x=torch.tensor([[100, 2, 421, 508], [491, 998, 1, 221]], dtype=torch.long)\n",
    "emb = Embeddings(d_model, vocab)\n",
    "embr = emb(x)\n",
    "print(embr.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 15.7212,   4.3630, -33.6761,  ...,  -0.7592, -13.7363,  11.5903],\n",
       "         [ 12.0749,  10.4128, -10.1933,  ...,  34.7949,  -3.0311,  17.0476],\n",
       "         [  0.6647,  20.0967,   9.4181,  ...,  48.3645,  -0.1343,  26.2912],\n",
       "         [-35.6708,  15.1472, -10.3006,  ...,   3.2073, -56.4983,  -1.2893]],\n",
       "\n",
       "        [[-31.4328, -15.9547,  15.4086,  ...,  30.6488, -15.2330,  -2.4861],\n",
       "         [ 64.8109,   2.7769, -44.1796,  ...,   9.1904,   8.7161,  -3.3016],\n",
       "         [-28.0984,  26.3967, -23.8661,  ...,   1.1219, -28.1064,  46.9230],\n",
       "         [  0.7738,  -6.5431,  -9.1307,  ...,  21.3106, -16.5163,  17.5139]]],\n",
       "       grad_fn=<MulBackward0>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#已知如下语料,给出其词嵌入\n",
    "corpus = [\"he is an old worker\", \"english is a useful tool\", \"the cinema is far away\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 语言模型\n",
    "\n",
    "词嵌入的基本原理是使用神经网络模型学习词与词之间的关系,常见的模型有连续词袋模型(CBOW)和跳过语рам模型(skip-gram)。这些模型的目标是最大化一个词的上下文词出现的概率。\n",
    "\n",
    "## N-gram语言模型\n",
    "\n",
    "N-gram语言模型是自然语言处理中一种重要的语言模型,它通过计算语言序列中连续N个词的联合概率来建模语言。\n",
    "\n",
    "具体来说,N-gram模型假设词的出现只与前N-1个词相关。例如,在双gram(N=2)模型中,词$w_i$的条件概率可以表示为:\n",
    "\n",
    "$P(w_i|w_{i-1})$\n",
    "\n",
    "在trigram(N=3)模型中,词$w_i$的条件概率为:\n",
    "\n",
    "$P(w_i|w_{i-1},w_{i-2})$ \n",
    "\n",
    "一般地,N-gram模型中词$w_i$的条件概率为:\n",
    "\n",
    "$P(w_i|w_{i-1},...,w_{i-N+1})$\n",
    "\n",
    "根据链式法则,语言序列中所有词连乘的联合概率可以表示为:\n",
    "\n",
    "$P(w_1, ..., w_M) = \\prod_{i=1}^{M} P(w_i|w_{i-1},...,w_{i-N+1})$\n",
    "\n",
    "其中M是词序列长度。\n",
    "\n",
    "N-gram模型通过统计语料中N个词共现的频率来估计条件概率$P(w_i|w_{i-1},...,w_{i-N+1})$。通常采用最大似然估计或平滑技巧来解决数据稀疏性问题。\n",
    "\n",
    "N-gram建模简单易实现,可有效模拟语言局部词序列模式。但无法捕捉长距离依赖关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(['forty', 'When'], 'winters'), (['winters', 'forty'], 'shall'), (['shall', 'winters'], 'besiege')]\n"
     ]
    }
   ],
   "source": [
    "CONTEXT_SIZE = 2\n",
    "EMBEDDING_DIM = 10\n",
    "torch.manual_seed(1)\n",
    "# We will use Shakespeare Sonnet 2\n",
    "test_sentence = \"\"\"When forty winters shall besiege thy brow,\n",
    "And dig deep trenches in thy beauty's field,\n",
    "Thy youth's proud livery so gazed on now,\n",
    "Will be a totter'd weed of small worth held:\n",
    "Then being asked, where all thy beauty lies,\n",
    "Where all the treasure of thy lusty days;\n",
    "To say, within thine own deep sunken eyes,\n",
    "Were an all-eating shame, and thriftless praise.\n",
    "How much more praise deserv'd thy beauty's use,\n",
    "If thou couldst answer 'This fair child of mine\n",
    "Shall sum my count, and make my old excuse,'\n",
    "Proving his beauty by succession thine!\n",
    "This were to be new made when thou art old,\n",
    "And see thy blood warm when thou feel'st it cold.\"\"\".split()\n",
    "# we should tokenize the input, but we will ignore that for now\n",
    "# build a list of tuples.\n",
    "# Each tuple is ([ word_i-CONTEXT_SIZE, ..., word_i-1 ], target word)\n",
    "ngrams = [\n",
    "    (\n",
    "        [test_sentence[i - j - 1] for j in range(CONTEXT_SIZE)],\n",
    "        test_sentence[i]\n",
    "    )\n",
    "    for i in range(CONTEXT_SIZE, len(test_sentence))\n",
    "]\n",
    "# Print the first 3, just so you can see what they look like.\n",
    "print(ngrams[:3])\n",
    "\n",
    "vocab = set(test_sentence)\n",
    "word_to_ix = {word: i for i, word in enumerate(vocab)}\n",
    "\n",
    "\n",
    "class NGramLanguageModeler(nn.Module):\n",
    "\n",
    "    def __init__(self, vocab_size, embedding_dim, context_size):\n",
    "        super(NGramLanguageModeler, self).__init__()\n",
    "        self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n",
    "        self.linear1 = nn.Linear(context_size * embedding_dim, 128)\n",
    "        self.linear2 = nn.Linear(128, vocab_size)\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        embeds = self.embeddings(inputs).view((1, -1))\n",
    "        out = F.relu(self.linear1(embeds))\n",
    "        out = self.linear2(out)\n",
    "        log_probs = F.log_softmax(out, dim=1)\n",
    "        return log_probs\n",
    "\n",
    "loss_function = nn.NLLLoss()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CBOW(Continuous Bag of Words)和skip-gram\n",
    "\n",
    "CBOW(Continuous Bag of Words)和skip-gram都是词向量训练算法,两者的主要区别如下:\n",
    "\n",
    "**CBOW**\n",
    "\n",
    "- 输入是上下文词,预测目标词\n",
    "- 输入层到隐层投影是一个连续的词袋(不区分词顺序)\n",
    "- 目标是根据上下文词预测当前词\n",
    "\n",
    "**skip-gram**\n",
    "\n",
    "- 输入是中心词,预测上下文词 \n",
    "- 从输入词向量映射到输出词向量 \n",
    "- 目标是通过当前词预测上下文\n",
    "\n",
    "具体来说:\n",
    "\n",
    "**CBOW**  \n",
    "\n",
    "给定一个词序列(w1, w2, w3, ..., wT),CBOW 的目标是最大化给定上下文词预测当前词wt的概率:\n",
    "\n",
    "P(wt | wt-k, ..., wt+k)\n",
    "\n",
    "其中wt-k,...,wt+k为wt的上下文词窗口。\n",
    "\n",
    "**skip-gram**\n",
    "\n",
    "给定一个词序列(w1, w2, w3, ..., wT),skip-gram 的目标是最大化通过当前词wt预测上下文词wj的概率:\n",
    "\n",
    "P(wj | wt) \n",
    "\n",
    "其中wj为wt的上下文词。\n",
    "\n",
    "CBOW 通过上下文词预测当前词,更关注上下文的语义信息。skip-gram 通过当前词预测上下文,更关注中心词的语义信息。两者分别从不同方面学到词向量的语义信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"27.png\" width=\"800\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"27.png\",width=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(['are', 'We', 'to', 'study'], 'about'), (['about', 'are', 'study', 'the'], 'to'), (['to', 'about', 'the', 'idea'], 'study'), (['study', 'to', 'idea', 'of'], 'the'), (['the', 'study', 'of', 'a'], 'idea')]\n"
     ]
    }
   ],
   "source": [
    "CONTEXT_SIZE = 2  # 2 words to the left, 2 to the right\n",
    "raw_text = \"\"\"We are about to study the idea of a computational process.\n",
    "Computational processes are abstract beings that inhabit computers.\n",
    "As they evolve, processes manipulate other abstract things called data.\n",
    "The evolution of a process is directed by a pattern of rules\n",
    "called a program. People create programs to direct processes. In effect,\n",
    "we conjure the spirits of the computer with our spells.\"\"\".split()\n",
    "\n",
    "# By deriving a set from `raw_text`, we deduplicate the array\n",
    "vocab = set(raw_text)\n",
    "vocab_size = len(vocab)\n",
    "\n",
    "word_to_ix = {word: i for i, word in enumerate(vocab)}\n",
    "data = []\n",
    "for i in range(CONTEXT_SIZE, len(raw_text) - CONTEXT_SIZE):\n",
    "    context = (\n",
    "        [raw_text[i - j - 1] for j in range(CONTEXT_SIZE)]\n",
    "        + [raw_text[i + j + 1] for j in range(CONTEXT_SIZE)]\n",
    "    )\n",
    "    target = raw_text[i]\n",
    "    data.append((context, target))\n",
    "print(data[:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 位置编码\n",
    "\n",
    "以前的方法-表格型\n",
    "\n",
    "* 方法一：使用[0,1]范围分配\n",
    "\n",
    "这个方法的分配方式是，将0-1这个范围的，将第一个token分配0，最后一个token分配去1，其余的token按照文章的长度平均分配。具体形式如下：\n",
    "\n",
    "我喜欢吃洋葱 【0 0.16 0.32.....1】\n",
    "\n",
    "我真的不喜欢吃洋葱【0 0.125 0.25.....1】\n",
    "\n",
    "问题：我们可以看到，如果句子长度不同，那么位置编码是不一样，所以无法表示句子之间有什么相似性。\n",
    "\n",
    "* 方法二：1-n正整数范围分配\n",
    "\n",
    "这个方法比较直观，就是按照输入的顺序，一次分配给token所在的索引位置。具体形式如下：\n",
    "\n",
    "我喜欢吃洋葱 【1，2，3，4，5，6】\n",
    "\n",
    "我真的不喜欢吃洋葱【1，2，3，4，5，6，7】\n",
    "\n",
    "问题：往往句子越长，后面的值越大，数字越大说明这个位置占的权重也越大，这样的方式无法凸显每个位置的真实的权重。\n",
    "\n",
    "\n",
    "https://www.cnblogs.com/ghj1976/p/li-jietransformer-de-wei-zhi-bian-ma.html\n",
    "\n",
    "https://blog.csdn.net/Kaiyuan_sjtu/article/details/119621613"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"31.jpg\" width=\"800\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"31.jpg\",width=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"32.jpg\" width=\"800\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url= \"32.jpg\",width=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transformer算法中的位置编码与其他方法的比较（chatgpt）\n",
    "\n",
    "Transformer算法中的位置编码(Positional Encoding)与RNN、LSTM中处理位置信息的方法有以下不同:\n",
    "\n",
    "1. Transformer的位置编码利用正弦、余弦函数将位置信息编码到词向量中,使模型对相对距离高度敏感。\n",
    "\n",
    "2. RNN、LSTM通过序列传递隐状态的方式来表示位置信息,位置信息被编码在隐状态中。\n",
    "\n",
    "3. 位置编码可以完全并行计算,计算效率更高。RNN、LSTM的顺序计算方式计算效率较低。\n",
    "\n",
    "4. 位置编码为模型带来一定的位置不变性,使模型对位置信息稍有变化更鲁棒。RNN和LSTM对位置变化更敏感。\n",
    "\n",
    "5. 通过周期性函数,位置编码使模型更好地学习到位置信息,相比RNN、LSTM直接使用位置索引更有效。\n",
    "\n",
    "6. 位置编码避免了位置信息过于绝对化,对位置稍有变化更加鲁棒。RNN和LSTM位置信息较为绝对。\n",
    "\n",
    "7. RNN、LSTM隐状态囊括了所有历史信息,计算资源利用率不高且训练难度较大。位置编码只编码位置,节省资源。\n",
    "\n",
    "综上,位置编码与RNN、LSTM顺序信息表示方法有不同优点。前者计算效率更高,对位置稍有偏移更鲁棒,是Transformer理解语序的关键。\n",
    "\n",
    "Transformer算法中的位置编码(Positional Encoding)与RNN、LSTM处理顺序信息的方法具有以下优缺点:\n",
    "\n",
    "**位置编码的优点:**\n",
    "\n",
    "- 计算效率高,可以完全并行\n",
    "- 为模型带来一定位置不变性,对小的位置变化更鲁棒\n",
    "- 使用周期函数,使模型更好地学习位置信息\n",
    "- 避免位置信息过于绝对化\n",
    "- 较RNN和LSTM更简单,只编码位置信息\n",
    "\n",
    "**位置编码的缺点:**\n",
    "\n",
    "- 需要设置最大序列长度,长度过长会导致计算复杂度提升\n",
    "- 仅编码相对位置,无法表示绝对位置\n",
    "- 对语序变化不如RNN等顺序敏感\n",
    "- 需搭配注意力机制,单独使用效果较弱\n",
    "\n",
    "**RNN和LSTM的优点:** \n",
    "\n",
    "- 可以表示绝对位置信息\n",
    "- 对语序变化更敏感\n",
    "- LSTM可以捕捉长程依赖关系\n",
    "\n",
    "**RNN和LSTM的缺点:**\n",
    "\n",
    "- 计算复杂度高,无法并行计算\n",
    "- 对位置信息过于敏感\n",
    "- 隐状态编码了所有历史,计算资源利用率低\n",
    "\n",
    "总之,位置编码更高效并具有一定位置鲁棒性,但RNN和LSTM可以建模绝对位置并对语序更敏感。两者互补的特点可根据任务需求进行选择。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.00000000e+00  1.00000000e+00  0.00000000e+00 ...  1.00000000e+00\n",
      "   0.00000000e+00  1.00000000e+00]\n",
      " [ 8.41470985e-01  5.40302306e-01  7.61720408e-01 ...  9.99999991e-01\n",
      "   1.15478198e-04  9.99999993e-01]\n",
      " [ 9.09297427e-01 -4.16146837e-01  9.87046251e-01 ...  9.99999964e-01\n",
      "   2.30956395e-04  9.99999973e-01]\n",
      " ...\n",
      " [ 1.23573123e-01 -9.92335469e-01  1.39920673e-01 ...  9.99980359e-01\n",
      "   5.42744868e-03  9.99985271e-01]\n",
      " [-7.68254661e-01 -6.40144339e-01 -6.63571724e-01 ...  9.99979514e-01\n",
      "   5.54292514e-03  9.99984638e-01]\n",
      " [-9.53752653e-01  3.00592544e-01 -9.99784705e-01 ...  9.99978652e-01\n",
      "   5.65840153e-03  9.99983991e-01]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1228.8x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plot\n",
    "\n",
    "def getPositionEncoding(seq_len,dim,n=10000):\n",
    "  PE = np.zeros(shape=(seq_len,dim))\n",
    "  for pos in range(seq_len):\n",
    "    for i in range(int(dim/2)):\n",
    "      denominator = np.power(n, 2*i/dim)\n",
    "      PE[pos,2*i] = np.sin(pos/denominator)\n",
    "      PE[pos,2*i+1] = np.cos(pos/denominator)\n",
    "\n",
    "  return PE\n",
    "\n",
    "PE = getPositionEncoding(seq_len=50, dim=128, n=10000)\n",
    "print(PE)\n",
    "\n",
    "caxes = plot.matshow(PE,interpolation ='nearest')\n",
    "plot.colorbar(caxes) \n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* transformer中的实现\n",
    "\n",
    "https://blog.csdn.net/qq_52785473/article/details/124537101\n",
    "\n",
    "\n",
    "1. unsqueeze\n",
    "\n",
    "2. Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4])\n",
      "1\n",
      "[1. 2. 3. 4.]\n",
      "tensor([[1., 2., 3., 4.]])\n",
      "torch.Size([1, 4])\n",
      "2\n",
      "[[1. 2. 3. 4.]]\n",
      "tensor([[1.],\n",
      "        [2.],\n",
      "        [3.],\n",
      "        [4.]])\n",
      "torch.Size([4, 1])\n",
      "2\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m在当前单元格或上一个单元格中执行代码时 Kernel 崩溃。请查看单元格中的代码，以确定故障的可能原因。有关详细信息，请单击 <a href='https://aka.ms/vscodeJupyterKernelCrash'>此处</a>。有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
     ]
    }
   ],
   "source": [
    "# unsqueeze函数使用案例\n",
    "x = torch.Tensor([1,2,3,4]) #torch.Tensor是默认的tensor类型\n",
    "print(x.size())  # torch.Size([4])\n",
    "print(x.dim())  # dimension的个数为1\n",
    "print(x.numpy())  # [1. 2. 3. 4.]\n",
    "\n",
    "print(torch.unsqueeze(x, 0))  # tensor([[1., 2., 3., 4.]])\n",
    "######  等价于torch.unsqueeze(x,-2) ######\n",
    "print(torch.unsqueeze(x, 0).size())  # torch.Size([1, 4])\n",
    "print(torch.unsqueeze(x, 0).dim())  # dimension的个数为2\n",
    "print(torch.unsqueeze(x, 0).numpy())  # [[1. 2. 3. 4.]]\n",
    "\n",
    "print(torch.unsqueeze(x, 1))\n",
    "###### 等价于torch.unsqueeze(x,-1)  ######\n",
    "# tensor([[1.],\n",
    "#         [2.],\n",
    "#         [3.],\n",
    "#         [4.]])\n",
    "print(torch.unsqueeze(x, 1).size())  # torch.Size([4, 1])\n",
    "print(torch.unsqueeze(x, 1).dim())  # dimension的个数为2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 1.5934,  1.1530,  1.5504,  0.3990,  0.2831, -1.0402,  1.6277, -0.1046,\n",
      "         0.8334,  0.4784, -2.1325, -1.5482,  0.9300, -0.4636,  0.6696, -0.1754])\n",
      "tensor([ 1.9917,  1.4412,  1.9380,  0.4987,  0.3539, -1.3002,  2.0346, -0.1308,\n",
      "         0.0000,  0.5980, -2.6656, -1.9353,  1.1625, -0.5795,  0.8370, -0.0000])\n"
     ]
    }
   ],
   "source": [
    "# Dropout函数\n",
    "m = nn.Dropout(p=0.2)\n",
    " input = torch.randn(20, 16)\n",
    " output = m(input)\n",
    "\n",
    " print(input[0])\n",
    " print(output[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "    def __init__(self, d_model, dropout, max_len=5000):\n",
    "    # d_model:词嵌入维度\n",
    "    # dropout:置零比率\n",
    "    # max_len:每个句子最大的长度\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        pe = torch.zeros(max_len, d_model)\n",
    "        position = torch.arange(0,  max_len).unsqueeze(1)\n",
    "        div_term = torch.exp(torch.arange(0, d_model, 2) * -(math.log(1000.0) / d_model))\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "        pe = pe.unsqueeze(0)\n",
    "        self.register_buffer(\"pe\", pe)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x + Variable(self.pe[:, :x.size(1)], requires_grad=False)\n",
    "        return self.dropout(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0],\n",
       "        [1],\n",
       "        [2],\n",
       "        [3],\n",
       "        [4],\n",
       "        [5]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.arange(0,  6).unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2, 4, 512])\n"
     ]
    }
   ],
   "source": [
    "dropout = 0.1\n",
    "max_len = 60\n",
    "pe = PositionalEncoding(d_model, dropout, max_len)\n",
    "pe_result = pe(embr)\n",
    "print(pe_result.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pe_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x203ade30730>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "pe = PositionalEncoding(20, 0)\n",
    "# 传入全0参数，相当于展示位置编码\n",
    "y = pe(Variable(torch.zeros(1, 100, 20)))\n",
    "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n",
    "plt.legend([\"dim %d\" %p for p in [4, 5, 6, 7]])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
